1. Field of the Invention
The present invention generally relates to a method and apparatus for classifying sleep recordings, and more particularly to a method and apparatus for classifying electroencephalogram sleep recordings into sleep stages using conditional random fields and subject adaptation.
2. Description of the Related Art
Sleep is indispensable to everybody. About one-third of Americans exhibit some kind of sleep problem. Hence, the study of sleep patterns, much of which is through sleep recordings, has consistently been an important research topic.
A typical sleep recording has one or more channels of electroencephalogram (EEG) waves coming from electrodes. Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages (e.g., wake, sleep) continuously. This task is crucial for the diagnosis and treatment of various sleep disorders. In addition, it relates closely to brain-machine interfaces, where successful classification can facilitate disabled people to control computers. Sleep staging is also of special interest to the study of avian bird song system and the evolutionary theory of mammalian sleep.
Many statistical pattern recognition methods, such as autoregression, and hidden Markov model (HMM), have been used to build an automatic, online sleep stager. Despite all these efforts, existing sleep stagers can only achieve average classification accuracy below 80%, which is insufficient for physicians to diagnose sleep disorders correctly. (In brain-computer interfaces, incorrect EEG wave classification can cause computers to receive wrong instructions.)